C1000-142 Exam Discussions & Posts
Madhuree Chennareddy
on: January 04, 2025
IBM cloud exams
Anonymous
fabulmator01@gmail.com
on: August 25, 2023
Reopen now please
Anonymous
Draco
on: July 19, 2023
Please reopen it now ..it's really urgent
UNITED STATES
AI Explanation - verified by User2971
on: June 27, 2026
Question 133:
- Correct answer: Layer 3 intercontroller roaming (option B)
- Why: If a WLC has Mobility Role Anchor configured, that controller acts as the anchor for roaming clients. When the client roams to another WLC (intercontroller roam), the anchor preserves the session and traffic is routed back through the anchor, enabling Layer 3 mobility across controllers (i.e., intercontroller roaming with L3 behavior).
- Why the other options don’t fit:
- Indirect: not a standard Cisco roaming term in this context.
- Intracontroller: roaming within the same controller (not intercontroller).
- Layer 2 intercontroller: intercontroller roaming, but L2 mobility would not involve the anchor-based L3 mobility behavior described here.
Plano, United States
AI Explanation - verified by User2971
on: June 27, 2026
Question 132:
- Correct answer: Mobility Express (option D)
- Why: Mobility Express is Cisco’s deployment model for small branches that provides a built-in controller on the APs, enabling CAPWAP-based management without a separate external WLC. This setup gives local management control at the branch while still using CAPWAP to manage and coordinate multiple APs.
- Why the other options don’t fit:
- Local mode autonomous: APs operate independently with no CAPWAP controller, so there isn’t centralized (or CAPWAP-based) management.
- SD-Access: This is Cisco’s fabric-driven, large-scale enterprise solution, not a small-branch local-management model.
- “Wireless” (in this context) isn’t a distinct deployment model like Mobility Express; the scenario specifically points to Mobility Express for local management with CAPWAP.
Plano, United States
AI Explanation - verified by raushankumar6343
on: June 27, 2026
Question 68:
Question 68 asks how to ensure only specific resources can access an Azure AI Service CSAccount1, with minimal admin effort and no external access.
Answer: A and E
Why these two work:
- A. In VNet1, enable a service endpoint for CSAccount1.
- This allows traffic from resources in the VNet to reach CSAccount1 over the Azure backbone, rather than over the public internet. It’s the first step to confine access to the virtual network.
- E. In CSAccount1, modify the virtual network settings.
- After enabling the endpoint, restrict CSAccount1 to “Selected networks” (or equivalent) and specify VNet1 (and its subnet). This blocks external/public access and ensures only the chosen VNet/subnet can reach the service.
Why the other options aren’t part of the two-answer solution:
- B (IAM) manages control plane access, not data-plane network access.
- C (modify VNet settings) is too generic and not needed to achieve network-restricted access.
- D (create a subnet) would be necessary only if a subnet didn’t exist; the question already aims for a minimal-effort solution, and A+E suffices if a subnet exists.
Key concept: use a service endpoint to connect the VNet to the Cognitive Service and then lock the CSAccount1 to selected networks.
Kolkata, India
AI Explanation - verified by raushankumar6343
on: June 27, 2026
Question 66:
For question 66, the recommended choice is D: Univariate Anomaly Detection.
Why:
- You’re monitoring a single data stream (temperature). Univariate anomaly detection analyzes one time series and flags unusual values automatically, with minimal setup.
- It’s the simplest option that still detects atypical values without building a multi-variable model.
Why the others aren’t as suitable:
- A: Multivariate Anomaly Detection is overkill here since you don’t have multiple interdependent streams to consider.
- B: Azure Stream Analytics metric alerts in Azure Monitor are threshold-based, not true anomaly detection; they require predefined thresholds and may miss subtle or seasonally changing anomalies.
- C: The option isn’t about anomaly detection and would add unnecessary complexity.
How you might implement quickly:
- Use a built-in anomaly detector for a single variable, feed it your temperature stream, and trigger alerts (via Azure Monitor, Logic Apps, or Functions) when anomalies are detected.
- Tune the sensitivity to balance false positives/negatives.
Kolkata, India
AI Explanation - verified by raushankumar6343
on: June 27, 2026
Question 63:
- Correct answer: D — virtual network rules
Explanation:
- You want to restrict access to the Language service so only specific Azure processes can reach it, with minimal admin effort.
- Virtual network rules let you specify which VNets are allowed to access the Cognitive Services endpoint, providing network-level access control without complex configuration.
- Why not the others:
- IPsec rules: are for VPN connectivity, not fine-grained service access control.
- Azure Application Gateway: manages HTTP/S traffic, not direct access control to Cognitive Services.
- A virtual network gateway: used for connecting networks (VPN/ExpressRoute), not for restricting access to the service itself.
Kolkata, India
AI Explanation - verified by raushankumar6343
on: June 27, 2026
Question 61:
- Correct answer: A — Run the Bot Framework Emulator.
Explanation:
- The Bot Framework Emulator is the local testing tool for bots built with the Bot Framework SDK. It lets you validate the bot running on your own machine before you connect to any channels or deploy to Azure.
- How it helps: you simulate conversations, inspect messages and payloads, and verify dialog flows and logic against your local bot endpoint.
- Typical flow: run the bot locally (e.g., on http://localhost:3978/api/messages), open the Emulator, configure a new bot with that endpoint (and AppId/AppPassword if needed), then start a chat to validate behavior.
- Why not the other options:
- Compose is for bot design/testing environments but not the standard local runtime validator before connecting to channels.
- Registering with Azure Bot Service is for deployment to Azure, not local validation.
- Windows Terminal is just a shell, not a testing tool.
Kolkata, India
AI Explanation - verified by User2971
on: June 27, 2026
Question 126:
Answer: D
Reasoning:
- SW3 has all access ports in VLAN 50, so it has no ports in VLAN 60.
- VTP pruning removes traffic for VLANs that have no active ports on downstream switches. If pruning is enabled in the VTP domain (and SW1 is the VTP server), VLAN 60 traffic will be pruned from trunks toward SW3.
- Therefore, SW3 will only receive frames from VLAN 50.
Why the other options aren’t correct:
- A: SW1(config)#vtp mode transparent — makes SW1 ignore VTP updates; pruning won’t function domain-wide.
- B: SW3(config)#vtp mode transparent — SW3 would not participate properly in VTP domain traffic, defeating pruning.
- C: SW2(config)#vtp pruning — pruning needs to be active in the VTP domain (server/clients); pruning on SW2 alone may not propagate as needed.
Plano, United States
AI Explanation - verified by User2971
on: June 27, 2026
Question 119:
Here’s the gist of Question 119 and why the chosen option (B) matters.
- Topic: Spanning Tree Protocol (STP) port-priority on a link with Link1 (copper) and Link2 (fiber). The fiber link should be the primary forwarding path, but on SW2 the fiber port is currently blocked.
- Key concept: For a given network segment, STP selects a designated port—the port on the segment that will forward toward the segment. The decision is made using the Port ID (Port Priority + Port Number). The lower Port ID wins; if costs to the root are equal, the port with the lower Port ID becomes designated.
- What the fix does: You influence which end of the Link2 segment becomes the designated port by adjusting the Port Priority on the ends of that link.
- The correct choice is: Enter spanning-tree port-priority 32 on SW1 (the end of Link2 opposite SW2). Lowering SW1’s port-priority on that link makes SW1’s Port ID smaller than SW2’s on that link, which causes the designated/forwarding role to align with the desired path.
- Why the SW2 change alone didn’t suffice: Simply changing SW2’s port-priority doesn’t guarantee SW1’s end won’t still win the designated role, depending on root paths and costs. Adjusting SW1’s end to a lower value ensures the intended designated-port outcome.
In short, by lowering SW1’s port-priority on the Link2 interface, you steer which end becomes the designated port for that link, resolving the blocked fiber path.
Plano, United States
AI Explanation - verified by raushankumar6343
on: June 27, 2026
Question 57:
- Why: To authenticate to the Speech service with an Azure AD (Entra) token, the service must be exposed via a custom subdomain and be reachable over a private endpoint. The Speech service uses custom subdomains with private endpoints for AAD-token authentication.
- What the two actions mean:
- Configure a custom subdomain: Sets up a dedicated, recognized domain for the Speech resource to issue and validate AAD tokens.
- Create a private endpoint: Enables private network connectivity to the Speech resource, allowing token-based access to stay within your network.
- Why the other options aren’t correct here:
- Enable a virtual network service endpoint: not required for AAD token authentication.
- Request an X.509 certificate: not needed for AAD token-based auth (certs are not the mechanism here).
- Create a Conditional Access policy: not a direct requirement to obtain or use the token in this scenario.
Kolkata, India
AI Explanation - verified by raushankumar6343
on: June 27, 2026
Question 56:
- Correct answer: B. Billing
- Why: For the Azure Cognitive Services container to start, you must supply three things: EULA acceptance, Billing, and ApiKey. Without these, the container won’t start. In this scenario, the question is asking which parameter must be included, and Billing is the required one among the options listed.
- What Billing means here: it indicates billing against your Azure subscription and ties the container usage to your resource.
- ApiKey note: the ApiKey you provide must be the key from the resource (Keys and Endpoints) page for your Anomaly Detector.
- Where to find keys: the API key is available on the Azure Cognitive Services resource keys page (and also in the LUIS portal’s Keys and Endpoints if applicable).
Kolkata, India
AI Explanation - verified by User2971
on: June 27, 2026
Question 110:
Question 110 describes a NAT setup with an internal network 10.1.1.0/27 and a public address pool 209.165.201.0/27.
- Correct answers: C and D.
Why:
- C: The 10.1.1.0/27 subnet is assigned as the inside local addresses. Inside local are the private addresses used on the internal network.
- D: Inside source addresses are translated to the 209.165.201.0/27 subnet. This is the inside global pool; internal hosts get translated to addresses in that public subnet as they reach the outside.
Why the other options aren’t correct:
- A: It states a one-to-one NAT translation. The scenario describes NAT to a public pool, which typically implies dynamic translation rather than a per-host static one-to-one mapping.
- B: The 209.165.201.0/27 subnet is assigned as the outside local address range. Outside local is the address the outside world sees for internal hosts, not the pool used for translation; this option is not accurate for this configuration.
Plano, United States
AI Explanation - verified by User2971
on: June 27, 2026
Question 107:
Question 107 discusses a port-channel between SW2 and SW3 where SW2 isn’t running Cisco IOS, and the channel doesn’t come up.
- Correct answer: B — The port-channel on SW2 is using an incompatible protocol.
Why:
- A port-channel requires both ends to negotiate the same channel protocol (e.g., Cisco’s PAgP or LACP from IEEE 802.3ad).
- If SW2 is not Cisco, it likely doesn’t implement Cisco’s PAgP, or it uses a different protocol. In that case SW3’s side cannot complete the negotiation, so the channel never forms.
Why the other options aren’t correct:
- A: “The port-channel mode should be set to auto.” Auto (PAgP) or active (LACP) negotiations won’t succeed if the other side doesn’t speak the same protocol.
- C: “The port-channel trunk is not allowing the native VLAN.” This is a post-formation VLAN issue, not the reason the channel fails to form.
- D: “The port-channel interface load balance should be set to src-mac.” Load-balancing mode affects traffic, not the initial establishment.
What to check:
- Ensure both sides support the same protocol (best practice: use LACP with
channel-group <n> mode active on Cisco gear).
- If mixing vendors, verify that the non-Cisco device supports the chosen protocol and configuration.
Plano, United States
AI Explanation - verified by User8952
on: June 27, 2026
Question 49:
Question 49 explanation:
- Correct answer: C — the virtual network settings for ta1.
- Why: To restrict access to a Cognitive Services resource to a specific virtual network, you configure the resource’s networking settings. In ta1, set the virtual network rules (select the VNet, e.g., vnet1) so only resources inside that VNet can access ta1.
- Why the other options aren’t correct:
- NSG for vnet1: Network security groups filter traffic at the network level but don’t specifically enforce access control to a particular Cognitive Services resource.
- Azure Firewall for vnet1: Similar to NSG, it controls traffic at the network boundary, not the per-resource access control for ta1.
- a Language service container for ta1: Not applicable here; Cognitive Services is a managed service, not a container you host yourself.
- Takeaway: Use the Language service’s Virtual Network settings to bind access to ta1 to vnet1.
Kolkata, India
AI Explanation - verified by User8952
on: June 27, 2026
Question 48:
Question 48 explanation:
- Why: For high-stakes decisions that affect staff finances, Responsible AI emphasizes human oversight and accountability. Adding a human review/approval step ensures fairness, auditability, and reduces the risk of incorrect or biased automated decisions.
- Why the other options are not correct:
- B: Do not automatically act on low-confidence results. If sentiment is uncertain, escalate or require human review rather than using it as the sole basis for payout.
- C: Do not use all surveys, including deletion requests. Respect data rights and privacy; avoid including data from customers who have asked for deletion.
- D: Do not publish raw survey data to staff. This poses privacy risks and lacks proper data governance.
In short, the right approach is to involve humans for decisions with financial impact and to uphold privacy and governance requirements.
Kolkata, India
AI Explanation - verified by User8952
on: June 27, 2026
Question 47:
Question 47 explanation:
- Correct sequence: Change the domain to General (compact) ? Retrain ? Export.
- The compact domain is the one that supports exporting so you can run the model offline (edge deployment). Non-compact/general domains don’t export to local devices.
- Changing the domain changes the model architecture, so you must retrain after the domain switch to generate a valid exported model.
- In the Custom Vision portal, go to project settings and change the Domain to General (compact), then save.
- Click Train to retrain the model with the new domain.
- Once training finishes, choose Export and select the desired format (e.g., TensorFlow/ONNX) for offline use, then download.
- Key takeaway: To export for an offline network, you must switch to a compact domain, retrain, and then export.
Kolkata, India
AI Explanation - verified by User2971
on: June 27, 2026
Question 105:
Question 105 asks: In a wireless network environment, what is calculated using the numerical values of the transmitter power level, cable loss, and antenna gain? The listed answer is C: EIRP.
Explanation:
- EIRP stands for Effective Isotropic Radiated Power. It represents the power that would be radiated in a given direction if the antenna were perfect (isotropic).
- It is calculated from the transmitter power, minus losses in the feedline (cable loss), plus the antenna gain. In dB terms:
EIRP(dBm) = P_tx(dBm) + G_antenna(dBi) - L_cable(dB).
- This quantity reflects how much power is actually available to propagate in a given direction.
- Other options:
- SNR (signal-to-noise ratio) depends on signal power relative to noise, not on transmitter power, cable loss, and antenna gain in a simple sum.
- RSSI is the received signal strength at the receiver, not a directional radiated power calculation.
- dBi is a unit of antenna gain, not a computed value.
So the correct concept tied to those inputs is EIRP.
Plano, United States
AI Explanation - verified by odili.tina
on: June 27, 2026
Question 47:
- Answer: The items you can endorse are Lakehouse1, Model1, and Report1 only (Dashboard1 cannot be endorsed).
- Why: Endorsement is a Fabric and Power BI feature used to mark high-quality content. The rule is that any Fabric or Power BI item except Power BI dashboards can be promoted or certified. Since Dashboard1 is a Power BI dashboard, it isn’t eligible for endorsement, per the note and explanation.
- Eligible: Lakehouse1, Model1, Report1
- Not eligible: Dashboard1
Lagos, Nigeria
AI Explanation - verified by User8648
on: June 27, 2026
Question 1:
Question 1 answer: A
Why:
- S3 Transfer Acceleration uses CloudFront’s globally distributed edge locations to optimize uploads directly to the target bucket, reducing latency for cross-continent transfers. This fits “aggregate data from all sites as quickly as possible” into a single S3 bucket with minimal ops.
- Using multipart uploads with the accelerated endpoint helps handle the large daily volume (500 GB per site) efficiently, enabling fast, reliable uploads of large objects.
- This approach avoids the overhead of cross-region replicas, batch devices, or multiple storage/services that require more orchestration.
Why the other options are weaker:
- B (Cross-Region Replication) adds lag and ongoing management; it won’t minimize operational overhead for a single central bucket.
- C (Snowball Edge) is batch/manual and slower for continuous ingestion.
- D (EC2/EBS with snapshots) adds complex cross-region data transfer and orchestration, not suited for rapid global ingestion into S3.
In short: enable S3 Transfer Acceleration on the destination bucket and use multipart uploads to ingest site data directly, which minimizes operational overhead while maximizing throughput.
Coimbatore, India
AI Explanation - verified by User8952
on: June 27, 2026
Question 21:
Correct answer: B
Explanation:
- In Language Understanding (LUIS), you have an authoring resource (used to create and train intents/entities) and a separate prediction resource (used for runtime inference).
- To add contributors, you grant them Azure RBAC access on the authoring resource via the Access control (IAM) page in the Azure portal. This controls who can edit and manage the authoring content.
- A Conditional Access policy governs sign-in requirements and access at the directory level, not per-resource collaboration.
- The prediction resource is for serving queries and its access is managed separately; you don’t add collaborators there for authoring work.
Kolkata, India
AI Explanation - verified by User8952
on: June 27, 2026
Question 20:
Answer: D and E — Azure Cognitive Search and Azure App Service
- Why: Provisioning a QnA Maker resource typically creates an App Service to host the QnA API/UI, and it also provisions a Cognitive Search service to enable fast search over the knowledge base.
- Why not the others:
- Language Understanding, Azure SQL Database, and Azure Storage are not automatically created as part of the standard QnA Maker provisioning in RG1.
- How to verify: In RG1, list resources and look for an
Azure App Service (host) and an Azure Cognitive Search service. This confirms the two automatically created resources.
Kolkata, India
AI Explanation - verified by User8952
on: June 27, 2026
Question 19:
Likely approach for Question 19:
- Presence detection (is the learner there?): Use the Face API (Vision) to detect faces in the video stream. You can also leverage head pose/emotion to infer attention.
- Identifying each learner: Use Speaker Recognition to identify who is speaking from the audio feed (voiceprints enrolled for each learner).
Why this fits:
- Video + audio: presence is best inferred from video facial detection, while identifying who each learner is can be done via audio with speaker identification.
- Minimal development: these two services provide out-of-the-box capabilities for detection and identification without building custom models.
Notes:
- If the hotspot options instead map identity to the Face API, you could enroll each learner in a Face API PersonGroup and use the Identify function. The pairing above emphasizes using audio for identification to complement the video presence check.
Kolkata, India
AI Explanation - verified by User8952
on: June 27, 2026
Question 17:
Answer: A – Anomaly Detector
- Why: The goal is to identify unusual values in time-series data (IoT readings) to predict failures. Anomaly Detector is a Azure Cognitive Service specifically designed to detect anomalies in time-series data, making it the right choice.
- Why not the others:
- Cognitive Search is for indexing and searching content, not detecting anomalies in time series.
- Form Recognizer extracts data from forms.
- Custom Vision is for image-based tasks.
- How to approach at scale: Treat each sensor stream as its own time series (you have 5,000 series). Feed each series to Anomaly Detector to flag deviations that may indicate impending maintenance needs.
Kolkata, India
AI Explanation - verified by User8952
on: June 27, 2026
Question 4:
Question 4: Correct answer is C: Form Recognizer.
Why this fits:
- Form Recognizer is built for automated data extraction from forms/receipts. It can identify and extract key data (e.g., vendor, transaction total) and return structured data with relationships, bounding boxes, and confidence.
- It includes prebuilt models for receipts, invoices, etc., which minimizes development effort since you don’t need to train from scratch.
Why the others aren’t correct:
- A. Custom Vision: for image classification/detection, not structured data extraction from forms.
- B. Personalizer: for personalized recommendations, not receipts processing.
- D. Computer Vision: provides general OCR, but not the specialized, structured extraction you get with Form Recognizer (no ready-made receipt model to pull vendor/total reliably).
Bottom line: use Form Recognizer to quickly and accurately extract top-level receipt data with minimal development.
Anonymous
AI Explanation - verified by User8952
on: June 27, 2026
Question 3:
Question 3: The correct answer is C: Language Understanding, Text Analytics, and QnA Maker.
Why this fits:
- Language Understanding handles natural language understanding across languages, enabling chit-chat and intent recognition.
- Text Analytics provides sentiment analysis (and language detection), addressing the sentiment requirement.
- QnA Maker supplies a knowledge-base/chat layer for known answers.
Why the other options are not correct:
- A (QnA Maker, Language Understanding, and Dispatch) includes Dispatch, which routes requests to multiple models. The exam expects the combination of L.U., Text Analytics, and QnA Maker for this scenario, and Dispatch isn’t needed to meet the stated requirements.
- B (Translator, Speech, and Dispatch) lacks sentiment analysis and a KB-based chat capability.
- D (Text Analytics, Translator, and Dispatch) lacks natural language understanding for routing/intent and a knowledge-base chat component.
Key takeaway: use L.U. for understanding, Text Analytics for sentiment, and QnA Maker for knowledge-base responses. Dispatch is not required for the described needs.
Anonymous
AI Explanation - verified by User8952
on: June 27, 2026
Question 2:
Here’s why Question 2 is correct and what the steps mean.
Correct sequence:
1) Export the versioned app’s package for containers (Export for container, GZIP) from the LUIS portal.
2) In the export, choose the latest deployable version of app1 (e.g., v1.1) to use as the mounted input for the container, along with its App ID.
3) Run the container and mount the model file (the packaged app) as input, using the required input mount and billing settings.
What each step does:
- Step 1 (Export for containers): Creates a container-ready package of the app version you want to deploy.
- Step 2 (Select v1.1): Ensures you deploy the latest trained/published version that’s intended for container use.
- Step 3 (Run container and mount): Launches the container with the app package mounted so the service can run, providing App ID and the necessary input mount and billing configuration.
In short: export the latest version as a container package, select that version (v1.1) for the container input, then run the container with the package mounted.
Anonymous
AI Explanation - verified by User8952
on: June 27, 2026
Question 1:
For Question 1, the correct choice is A: use the AddPhraseListAsync method with a PhraselistCreateObject.
Why:
- You want to update the same phrases across many models. A shared phrase list can be created once and reused by multiple models, which is exactly what a phrase list (Phraselist) with IsExchangeable set to true enables.
What the code shows:
- Box 1 uses
AddPhraseListAsync(appId, versionId, new PhraselistCreateObject { ... }) to create the phrase list.
- Box 2 uses the
PhraselistCreateObject type to supply the details: EnabledForAllModels = false, IsExchangeable = true, Name = "QuantityPhraselist", and the Phrases (e.g., "few,more,extra").
Result:
- A single phrase list is created that can be shared (exchanged) across the language models within the app version, allowing you to apply the new phrases to all 100 chatbots efficiently.
Anonymous
AI Explanation - verified by User2971
on: June 26, 2026
Question 100:
I can’t view the exhibit image, but here’s the concept behind Q100 and why the answer is B.
- GRE tunnel basics: A tunnel has a source and a destination. The destination can be a statically configured IP, or it can be learned/determined via the tunnel interface in dynamic setups (e.g., DMVPN or dynamic mappings).
- Why B: “The tunnel destination will be known via the tunnel interface.” This indicates the destination isn’t hard-coded in the config line you’re viewing; instead, it’s resolved through the tunnel interface (dynamic/Destination learned from routing or a dynamic mapping). The show run output would reflect that lack of a fixed destination (e.g., dynamic/destination learned).
- Why the other options are less likely:
- A: Keepalive mismatch would show as a problem, not something verified by a standard show run line about the destination.
- C: You can’t be sure it will work just from show run; dynamic/destination learning depends on underlying reachability and mappings.
- D: MTU default isn’t what this show run line would verify.
If you can share the exact show run lines, I can map them precisely to the concept.
Plano, United States
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on: June 26, 2026
Question 99:
I can’t view the exhibit image, but here’s the concept behind Q99 and why inter-VRF routing is needed.
- VRFs isolate routing tables. To let Customer2 hosts reach a server in Customer1 (192.168.1.200), you need inter-VRF routing or route leaking so the path to that server is known outside Customer1’s VRF.
- A common technique on Cisco devices is to leak a route from a VRF into the global routing table using:
- ip route vrf <VRF> <dest> <mask> <next-hop> global
This publishes the VRF’s route so devices in other VRFs (or in the global table) can learn how to reach it.
- In this question, option A represents leaking the Customer1 network (e.g., 172.16.1.0/24) into the global table. Once the route to the FTP server’s network is visible globally, Customer2 can forward traffic toward 192.168.1.200 via the next-hop.
- Why the other options aren’t correct: they either leak the wrong routes or operate in the wrong VRF context, preventing reachability.
If you can share a bit more detail about the exhibit (which networks/devices are in which VRFs), I can walk through the exact leakage steps for that topology.
Plano, United States
AI Explanation - verified by User2971
on: June 26, 2026
Question 98:
For question 98, the correct choice is C.
Why C works:
- The interface must be bound to the same VRF as the destination network. Here, the server 172.16.0.1 sits in VRF Bank, so the R1 interface should be in vrf forwarding bank.
- The OSPF process that runs for that VRF must also be in VRF Bank:
router ospf 44 vrf bank.
- Use a network statement that covers 172.16.0.0/16 inside that VRF:
network 172.16.0.0 0.0.255.255 area 0. The wildcard mask 0.0.255.255 matches 172.16.0.0–172.16.255.255, aligning with the /16 subnet you’re using (255.255.0.0 on the interface).
Why the other options are incorrect:
- A: The interface isn’t bound to VRF Bank, so OSPF in VRF Bank wouldn’t see it.
- B and D: Bind the interface or OSPF to a different VRF (Hotel), which would prevent reachability to the bank’s server.
- The combination in C ensures the interface is in VRF Bank and advertises the 172.16.0.0/16 route within that VRF.
Plano, United States
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on: June 26, 2026
Question 89:
Question 89 asks how to enable IPsec over GRE using IPsec profiles. The two correct changes are:
- C. Remove all configuration related to crypto map from R1 and R2 and eliminate the ACL.
- D. Create an IPsec profile, associate the transform-set, and apply the profile to the tunnel interface.
Why:
- IPsec profiles replace the old crypto-map/ACL method. When you use an IPsec profile, you configure a profile containing the transform-set and apply it directly to the GRE tunnel interface with something like
tunnel protection ipsec profile <name>. There’s no crypto map or ACL on the interface shaping which traffic is encrypted.
- Removing the crypto-map and ACL (C) clears the old mechanism, allowing the profile-based approach to take effect.
- Creating the IPsec profile and attaching it to the tunnel interface (D) is the core step to enable IPsec protection for GRE peers.
Notes:
- A and B describe crypto-map-based configurations and are not aligned with the profile-based approach.
- E is unrelated to the profile-based method.
Plano, United States
AI Explanation - verified by User2971
on: June 26, 2026
Question 86:
Question 86 asks how to assign the IP 192.168.1.1/24 to GigabitEthernet1. Here’s the idea:
- A)
Router(config-if)# ip address 192.168.1.1 255.255.255.0
- This is the standard command to configure an IPv4 address on an interface. It sets the interface’s address and subnet mask.
- B)
Router(config-vrf)# address-family ipv4
- This enters the IPv4 address-family context for a VRF. It only applies if you’re using a VRF and need to configure IPv4 routing within that VRF (for example, enabling IPv4 unicast routes in the VRF).
Why these two might be chosen:
- If the interface is part of a VRF, you typically configure the interface IP with the first command (A) and, in the VRF context, enable the IPv4 address family (B) so IPv4 routing works inside the VRF.
Note:
- In a plain, non-VRF setup, you usually just need A (and possibly
no shutdown if the interface is administratively down). The VRF-specific B command isn’t needed unless VRFs are being used.
If you want, I can walk through whether your network really uses a VRF and how that changes the steps.
Plano, United States
AI Explanation - verified by User2971
on: June 26, 2026
Question 61:
Here’s how to understand Question 61.
- Why A is correct: The vswitch is software-based. When a broadcast frame arrives, the vswitch interrupts the host CPU to switch context so it can process the packet. This CPU interruption is part of the processing overhead that contributes to broadcast radiation.
- Why B is correct: In virtualized environments, the Layer 2 domain can be very large, so a single broadcast frame may be delivered to many VMs. That amplification increases processing and overhead, i.e., broadcast radiation.
- Why C is not correct: It’s not accurate to say virtual machines communicate primarily through broadcast. VM-to-VM traffic is mainly unicast through the vSwitch; broadcast is used mainly for discovery (e.g., ARP), not as the primary communication method.
- D and E are not about why broadcasts radiate within the VM domain; they describe how traffic could be handled on the vSwitch-to-physical-switch path, which isn’t the core reason for broadcast radiation inside the VM environment.
Note: The answer key shown lists B and C, which conflicts with the Cisco concepts above. The A and B explanation aligns with how software-based vswitch processing and large L2 domains drive broadcast overhead.
Plano, United States
weekend_warrior_w
on: June 23, 2026
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Canada
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Canada